Identifying the most suitable machine learning approach for a road digital twin
Kun Chen, Mehran Eskandari Torbaghan, Mingjie Chu, Long Zhang, Álvaro García
Abstract
Road infrastructure systems have been suffering from ineffective maintenance strategies, exaggerated by budget restrictions. A more holistic road-asset-management approach enhanced by data-informed decision making through effective condition assessment, distress detection and future condition predictions can significantly enhance maintenance planning, prolonging asset life. Recent technology innovations such as digital twins have great potential to enable the needed approach for road condition predictions and proactive asset management. To this end, machine learning techniques have also demonstrated convincing capabilities in solving engineering problems. However, none of them has been considered specifically within a digital twin context. There is therefore a need to review and identify appropriate approaches for the usage of machine learning techniques with road digital twins. This paper provides a systematic literature review of machine learning algorithms used for road condition predictions and discusses findings within the road digital twin framework. The results show that existing machine learning approaches suitable and mature for stipulating successful road digital twin development. Moreover, the review, while identifying gaps in the literature, indicates several considerations and recommendations required on the journey to road digital twins and suggests multiple future research directions based on the review summaries of machine learning capabilities.